Biomedical subjects

Christopher Jennison

Publications and source records attributed to Christopher Jennison.

3 recordsLinked to original sources

PubMed2006

Efficient group sequential designs when there are several effect sizes under consideration.

We consider the construction of efficient group sequential designs where the goal is a low expected sample size not only at the null hypothesis and the alternative (taken to be the minimal clinically meaningful effect size), but also at more optimistic anticipated effect sizes. Pre-specified Type I error rate and power requirements can be achieved both by standard group sequential tests and by more recently proposed adaptive procedures. We investigate four nested classes of designs: (A) group sequential tests with equal group sizes and stopping boundaries determined by a monomial error spending function (the 'rho-family'); (B) as A but the initial group size is allowed to be different from the others; (C) group sequential tests with arbitrary group sizes and arbitrary boundaries, fixed in advance; (D) adaptive tests-as C but at each analysis, future group sizes and critical values are updated depending on the current value of the test statistic. By examining the performance of optimal procedures within each class, we conclude that class B provides simple and efficient designs with efficiency close to that of the more complex designs of classes C and D. We provide tables and figures illustrating the performances of optimal designs within each class and defining the optimal procedures of classes A and B.

Anticholesteremic Agents↗

PubMed2005

Meta-analyses and adaptive group sequential designs in the clinical development process.

The clinical development process can be viewed as a succession of trials, possibly overlapping in calendar time. The design of each trial may be influenced by results from previous studies and other currently proceeding trials, as well as by external information. Results from all of these trials must be considered together in order to assess the efficacy and safety of the proposed new treatment. Meta-analysis techniques provide a formal way of combining the information. We examine how such methods can be used in combining results from: (1) a collection of separate studies, (2) a sequence of studies in an organized development program, and (3) stages within a single study using a (possibly adaptive) group sequential design. We present two examples. The first example concerns the combining of results from a Phase IIb trial using several dose levels or treatment arms with those of the Phase III trial comparing the treatment selected in Phase IIb against a control This enables a "seamless transition" from Phase IIb to Phase III. The second example examines the use of combination tests to analyze data from an adaptive group sequential trial.

Algorithms↗

PubMed2003

Mid-course sample size modification in clinical trials based on the observed treatment effect.

It is not uncommon to set the sample size in a clinical trial to attain specified power at a value for the treatment effect deemed likely by the experimenters, even though a smaller treatment effect would still be clinically important. Recent papers have addressed the situation where such a study produces only weak evidence of a positive treatment effect at an interim stage and the organizers wish to modify the design in order to increase the power to detect a smaller treatment effect than originally expected. Raising the power at a small treatment effect usually leads to considerably higher power than was first specified at the original alternative. Several authors have proposed methods which are not based on sufficient statistics of the data after the adaptive redesign of the trial. We discuss these proposals and show in an example how the same objectives can be met while maintaining the sufficiency principle, as long as the eventuality that the treatment effect may be small is considered at the design stage. The group sequential designs we suggest are quite standard in many ways but unusual in that they place emphasis on reducing the expected sample size at a parameter value under which extremely high power is to be achieved. Comparisons of power and expected sample size show that our proposed methods can out-perform L. Fisher's 'variance spending' procedure. Although the flexibility to redesign an experiment in mid-course may be appealing, the cost in terms of the number of observations needed to correct an initial design may be substantial.

Clinical Trials, Phase III as Topic↗